Households with limited financial expertise sometimes attempt to avoid investment mistakes by delegating the management of their investments to experts. However, evidence on the efficacy of delegation has been mixed. This paper contributes to understanding the question: why is the acquired expertise of asset managers a limited substitute for investors’ lack of expertise? We consider an economy with investors (who vary in sophistication) and managers (who vary in skill). Unsophisticated investors’ lack of expertise makes it hard for them to distinguish skilled managers from unskilled ones. In the equilibrium that follows, investors exert little effort when searching for managers, leading to a suboptimal composition of managerial types entering the market. When unsophisticated investors are endowed with weak signals, they attempt to time their entry and exit from the market for managers, but their actions are predictable, so performance continues to suffer.

The standard heterogeneous autoregressive (HAR) model is perhaps the most popular benchmark model for forecasting return volatility. It is often estimated using raw realized variance (RV) and ordinary least squares (OLS). However, given the stylized facts of RV and well-known properties of OLS, this combination should be far from ideal. The aim of this paper is to investigate how the predictive accuracy of the HAR model depends on the choice of estimator, transformation, or combination scheme made by the market practitioner. In an out-of-sample study, covering the S&P 500 index and 26 frequently traded NYSE stocks, it is found that simple remedies systematically outperform not only standard HAR but also state of the art HARQ forecasts.

Using data from 2002 to 2013, we show that algorithmic trading has a positive impact on firm value. Most of this positive impact flows through the channels of stock liquidity, idiosyncratic volatility, and idiosyncratic skewness, but algorithmic trading also has a large economic effect outside those channels. We use the advent of auto quotation on the New York Stock Exchange as an exogenous shock to algorithmic trading to rule out reverse causality. The positive effects of algorithmic trading on firm value are stronger for larger firms and in the post-2007 period when algorithmic trading intensity is higher.